Statement of Work There is a fundamental gap in understanding the nature of interregional differences in health outcomes and longevity in the U.S.: people in certain U.S. states and counties live up to 3.5 years (males) and 4.6 years (females) less than in states with better health outcomes. The persistence, and even widening, of this gap over time represents an important problem for the U.S. health system. These geographic disparities are associated with increased burden of disease, increased health expenditures in the health-care system, and showcase an observed lag in health and longevity compared to other industrialized nations. The objective of this application is to identify the mechanisms underlying the observed geographic disparities and clarify the role clinic- and non-clinic-related factors play in them. We expect that these geographic differences can be observed in individual Medicare trajectories and that the size and scope of the 5% Medicare dataset supplemented by Medicare records linked to the Health and Retirement Study will allow us to discover the causes of such disparities. In this project, we will test four scenarios on how these disparities could be reflected in health measures extracted from Medicare data: the regions with lower life expectancy exhibit higher disease incidence (scenario #1), worse patient survival (scenario #2), higher multimorbidity (scenario #3), and/or worse health state of individuals aged 65 (i.e., at time of entry into the Medicare system) (scenario #4). In Aim 1 we will test in what extent each scenario (or their combinations) can explain observed geographic disparities in mortality. We will identify specific diseases that contribute most to health disparities through each of the scenarios to be studied. In Aim 2, we will identify how clinic-related characteristics such as use of specific treatments, treatment choice and adherence to treatment, utilization of screening and diagnostic procedures (especially for early-stage diagnostics) impact the health outcomes and contribute to geographic disparities. Finally, in Aim 3 we will identify how non-clinic-related factors such as socioeconomic, behavioral, environmental characteristics, and access to and quality of care measures (constructed from 5%-Medicare data) impact the clinical measures identified in Aim 2 as determinants of geographic disparities. The results will provide new knowledge about clinic- and non-clinic-related barriers to improvement of health and increase of life expectancy in underperforming U.S. regions, identify the most affected population groups, and explain the role of the clinic and non-clinic-related factors in morbidity and mortality trends. The results of the proposed study will make possible the next step in approaching our long-term goal: to improve strategies of disease prevention, optimize allocations of medical resources with the focus on underprivileged communities and to improve health care standards to slow down or stop the growing gap in health disparities in the U.S.